Predictors of Surrogate Decision Makers Selecting Life-Sustaining Therapy for Severe Acute Brain Injury Patients: An Analysis of US Population Survey Data.

BACKGROUND: Patients with a severe acute brain injury admitted to the intensive care unit often have a poor neurological prognosis. In these situations, a clinician is responsible for conducting a goals-of-care conversation with the patient's surrogate decision makers. The diversity in thought and background of surrogate decision makers can present challenges during these conversations. For this reason, our study aimed to identify predictive characteristics of US surrogate decision makers' favoring life-sustaining treatment (LST) over comfort measures only for patients with severe acute brain injury. METHODS: We analyzed data from a cross-sectional survey study that had recruited 1588 subjects from an online probability-based US population sample. Seven hundred and ninety-two subjects had randomly received a hypothetical scenario regarding a relative intubated with severe acute brain injury with a prognosis of severe disability but with the potential to regain some consciousness. Seven hundred and ninety-six subjects had been randomized to a similar scenario in which the relative was projected to remain vegetative. For each scenario, we conducted univariate analyses and binary logistic regressions to determine predictors of LST selection among available respondent characteristics. RESULTS: 15.0% of subjects selected LST for the severe disability scenario compared to 11.4% for the vegetative state scenario (p = 0.07), with those selecting LST in both groups expressing less decisional certainty. For the severe disability scenario, independent predictors of LST included having less than a high school education (adjusted OR = 2.87, 95% CI = 1.23-6.76), concern regarding prognostic accuracy (7.64, 3.61-16.15), and concern regarding the cost of care (4.07, 1.80-9.18). For the vegetative scenario, predictors included the youngest age group (30-44 years, 3.33, 1.02-10.86), male gender (3.26, 1.75-6.06), English as a second language (2.94, 1.09-7.89), Evangelical Protestant (3.72, 1.28-10.84) and Catholic (4.01, 1.72-9.36) affiliations, and low income (< $25 K). CONCLUSION: Several demographic and decisional characteristics of US surrogate decision makers predict LST selection for patients with severe brain injury with varying degrees of poor prognosis. Surrogates concerned about the cost of medical care may nevertheless be inclined to select LST, albeit with high levels of decisional uncertainty, for patients projected to have severe disabilities.

Concerns of surrogate decision makers for patients with acute brain injury: A US population survey.

OBJECTIVE: To determine whether groups of surrogates for patients with severe acute brain injury (SABI) with poor prognosis can be identified based on their prioritization of goals-of-care (GOC) decisional concerns, an online survey of 1,588 adults recruited via a probability-based panel representative of the US population was conducted. METHODS: Participants acted as a surrogate for a GOC decision for a hypothetical patient with SABI and were randomized to 1 of 2 prognostic scenarios: the patient likely being left with a range of severe functional disability (SD) or remaining in a vegetative state (VS). Participants prioritized a list of 12 decisional concerns via best-worst scaling. Latent class analysis (LCA) was used to discover decisional groups. RESULTS: The completion rate was 44.6%; data weighting was conducted to mitigate nonresponse bias. For 792 SD respondents, LCA revealed 4 groups. All groups shared concerns regarding respecting patient wishes and minimizing suffering. The 4 groups were otherwise distinguished by unique concerns that their members highlighted: an older adult remaining severely disabled (34.4%), family consensus (26.4%), doubt regarding prognostic accuracy (20.7%), and cost of long-term care (18.6%). For the 796 VS respondents, LCA revealed 5 groups. Four of the 5 groups had similar concern profiles to the 4 SD groups. The largest (29.0%) expressed the most prognostic doubt. An additional group (15.8%) prioritized religious concerns. CONCLUSIONS: Although surrogate decision makers for patients with SABI are concerned with respecting patient wishes and minimizing suffering, certain groups highly prioritize other specific decisional factors. These data can help inform future interventions for supporting decision makers.

Sex differences and attitudes toward living donor kidney transplantation among urban black patients on hemodialysis.

BACKGROUND AND OBJECTIVES: Living donor kidney transplantation, the treatment of choice for ESRD, is underused by women and blacks. To better understand sex differences in the context of potential barriers to living donor kidney transplantation, the Dialysis Patient Transplant Questionnaire was administered in two urban, predominantly black hemodialysis units. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The Dialysis Patient Transplant Questionnaire was designed to study barriers to kidney transplantation from previously validated questions. Between July of 2008 and January of 2009, the Dialysis Patient Transplant Questionnaire was administered to 116 patients on hemodialysis, including potentially eligible and ineligible living donor kidney transplantation candidates. Of 101 patients who self-identified as black or African American, 50 (49.5%) patients had the questionnaire entirely administered by the researcher or assistant, 25 (24.8%) patients required some assistance, and 26 (25.7%) patients completed the Dialysis Patient Transplant Questionnaire entirely by themselves. Multiple logistic regression methods were used to determine if the observed bivariate associations and differences persisted when controlled for potential confounders. RESULTS: Women were less likely to want living donor kidney transplantation compared with men (58.5% versus 87.5%, P=0.003), despite being nearly two times as likely as men to receive unsolicited offers for kidney transplant (73.2% versus 43.2%, P=0.02). They were also less likely to have been evaluated for a kidney transplant (28.3% versus 52.2%, P=0.01). The multiple logistic regression analysis showed that sex was a statistically significant predictor of wanting living donor kidney transplantation (women versus men odds ratio, 0.13; 95% confidence interval, 0.04 to 0.46), controlling for various factors known to influence transplant decisions. A sensitivity analysis indicated that mode of administration did not bias these results. CONCLUSIONS: In contrast to previous studies, the study found that black women were less likely to want living donor kidney transplantation compared with black men. Black women were also less likely to be evaluated for a kidney transplant, although they were more likely to receive an unsolicited living donor kidney transplantation offer.

Model-implied instrumental variable-generalized method of moments (MIIV-GMM) estimators for latent variable models.

The common maximum likelihood (ML) estimator for structural equation models (SEMs) has optimal asymptotic properties under ideal conditions (e.g., correct structure, no excess kurtosis, etc.) that are rarely met in practice. This paper proposes model-implied instrumental variable - generalized method of moments (MIIV-GMM) estimators for latent variable SEMs that are more robust than ML to violations of both the model structure and distributional assumptions. Under less demanding assumptions, the MIIV-GMM estimators are consistent, asymptotically unbiased, asymptotically normal, and have an asymptotic covariance matrix. They are "distribution-free," robust to heteroscedasticity, and have overidentification goodness-of-fit J-tests with asymptotic chi-square distributions. In addition, MIIV-GMM estimators are "scalable" in that they can estimate and test the full model or any subset of equations, and hence allow better pinpointing of those parts of the model that fit and do not fit the data. An empirical example illustrates MIIV-GMM estimators. Two simulation studies explore their finite sample properties and find that they perform well across a range of sample sizes.

Constrained versus unconstrained estimation in structural equation modeling.

Recently, R. D. Stoel, F. G. Garre, C. Dolan, and G. van den Wittenboer (2006) reviewed approaches for obtaining reference mixture distributions for difference tests when a parameter is on the boundary. The authors of the present study argue that this methodology is incomplete without a discussion of when the mixtures are needed and show that they only become relevant when constrained difference tests are conducted. Because constrained difference tests can hide important model misspecification, a reliable way to assess global model fit under constrained estimation would be needed. Examination of the options for assessing model fit under constrained estimation reveals that no perfect solutions exist, although the conditional approach of releasing a degree of freedom for each active constraint appears to be the most methodologically sound one. The authors discuss pros and cons of constrained and unconstrained estimation and their implementation in 5 popular structural equation modeling packages and argue that unconstrained estimation is a simpler method that is also more informative about sources of misfit. In practice, researchers will have trouble conducting constrained difference tests appropriately, as this requires a commitment to ignore Heywood cases. Consequently, mixture distributions for difference tests are rarely appropriate.